1. Field of the Invention
The present invention is based primarily on a neuroholographic model of how the brain stores and retrieves memories and relates to methods deriving therefrom for storing and retrieving temporal information, and applications thereof, including electronic, optical, magnetic and neural network devices that use the method of distributing temporal information into spatially ordered arrays including methods for detecting the efficacy of drugs, toxic substances or treatments on human memory and other cognitive processes, and the use of such detection for drug treatment or development.
2. Background and Brief Description of the Related Art
The invention is based on a new model of brain mechanisms in temporal memory storage and retrieval that derives from principles of brain anatomy and studies of brain electrophysiology. This new model follows loosely from a prior model the present inventor published of neuroholographic memory functions in the brain (Landfield, 1976). However, the prior model did not address storage of sequential information sets. Therefore, the elements of the updated model that deal with distribution and storage of temporal information represent a new concept that is not inherent in the prior model. The original model (Landfield, 1976) proposed that memory traces are formed in a neuron in which excitation generated by a non-information containing synchronous EEG wave occurs at approximately the same time as excitation from information-containing impulses arriving over other inputs. The summation of excitation from the two inputs is sufficient to activate the receiving neuron to fire impulses, which then leave lasting traces (memory) in that neuron as well as activates the next neurons in the chain. Because the model relies on summation between two brain waves, and projection of modified electrical waves, this process was noted to be partly analogous to the interference pattern-holographic process of optics (Landfield, 1976).
Although, the nature of neural information is of course substantially different from the phase information carried in object-reflected light beams of holograms, it was recognized that certain common principles might apply to many forms of information, storage and retrieval based on summation of two inputs. In the new model it was also recognized that each projected wave carried a time slice of information, followed by successive waves (time slices) at periodic intervals. This creates a storage problem for the brain because storage of multiple waves in the same neurons could result in confounding and disorganization of information. However, the modified waves that transport the encoded “time-slices” of information travel over the same fibers and are presented to the same receptive neurons, making its difficult to target successive information slices to different neuron storage sites.
Many brain models for processing temporal information have been proposed, but very few deal with long term memory storage of that information. Those that do often propose the storage of sequential information in different transient oscillatory patterns in regions of the same neurons, or in different activity patterns in linked cell assemblies. However, as noted, it is highly difficult or not feasible to store temporally-tagged information in the same neurons. Thus, there is a need for discovering how the brain automatically stores and retrieves temporally sequential data, as this would suggest new architectures for memory storing devices and would allow scientists to study memory processes more accurately for development of drugs and detection of toxicity or pathology.
At present, there are massive efforts underway at many pharmaceutical firms to develop new drugs for the improvement of memory, aimed at elderly or neurologically impaired individuals, and perhaps eventually at healthy young adults as well. One of the major problems of this drug development work, however, is that there are few rapid screening methods for testing efficacy of drugs on memory. The animal models used can be controversial and the data are not always generalizable to humans; in addition, the present cellular models being developed (e.g., long-term potentiation) are even more controversial (see Russo, “The Scientist” Vol 13, March, 1999) and, in any case, do not reflect processing in complex memory systems.
The model proposed here is believed to be at least accurate, such that it can generate reality-based methods for assessing sequential memory storage based on phase shifting, intensity of summation, rates of travel of excitation, and spatial distribution of neural excitation, either in in vitro organotypic brain slices, animals examined with standard electrode or optical receptor arrays, other animal preparations or in humans. Therefore the model and its predictions could potentially generate extremely sensitive and accurate screening procedures for development of drugs that influence memory and perhaps other cognitive processes. Moreover, the method could be used by defense, medical, environmental agencies, or companies to detect or evaluate neurotoxic agents that impair memory.
Many electronic memory systems (computers) rely on random access memories, in which information sets are stored in available sites and lose sequential information (other than date codes that must be interpreted by the user). However, in random access memory semiconductor devices, spatial encoding about the memory bank used and its location on the memory device (row and column), is kept in reference with the information stored for later retrieval. This is typically accomplished by row and column decoders. On the other hand, known sequential memories systems generally utilize a “First-in-First-out” architecture based on serial transfer data, and are termed “sequential access memory.” However, these designs are not optimal for long-term storage because data bits are not located in known addresses for extended periods. Thus, construction of new devices that could automatically learn, store and retrieve sequential information in a temporally ordered fashion without using complex addressing systems, therefore, might have vast utility at which we can only begin to guess. This temporal learning capacity might, for example, vastly improve computer graphics, reprogramming of devices based on experience of operation, architectural or industrial design, and self-organizing of learning and self-correcting instrument errors; numerous entertainment uses (computer games, holographic graphics, etc.) could also be envisioned.
In addition, there are intensive major efforts by defense and various research and industrial establishments to develop devices that can learn based on neural network principles. Clearly, the incorporation of a process for automatically learning and storing temporally ordered information in a readily retrievable sequential format might be a major advantage for these efforts.